EdgeModelKit: Sensor Data Acquisition and Logging Library

PreviousConsentiumNow Library Documentation NextCode usage

Last updated 4 months ago

Was this helpful?

EdgeModelKit: Sensor Data Acquisition and Logging Library

EdgeModelKit is a Python library developed by EdgeNeuron, designed to simplify sensor data acquisition, logging, and real-time processing for IoT devices. It works seamlessly with the DataLogger script from the , making it ideal for edge computing and machine learning applications.

Features

Serial Communication: Supports data acquisition over serial ports with robust error handling.
Flexible Data Fetching: Retrieve sensor data as Python lists or NumPy arrays.
Customizable Logging: Log sensor data into CSV files with optional timestamps and counters.
Class-Based Organization: Log data with class labels to prepare datasets for machine learning tasks.
Error Handling: Gracefully handles data decoding errors and missing keys in sensor data packets.

Usage Prerequisites

This library is designed to work with the DataLogger script available in the . The DataLogger script configures your Arduino-based IoT device to send structured JSON sensor data over a serial connection.

Before using EdgeModelKit, ensure:

Your Arduino device is programmed with the DataLogger script from the .
The device is connected to your system via a serial interface.

Installation

Install EdgeModelKit using pip:

pip install edgemodelkit

Quick Start

1. Initialize the DataFetcher

from edgemodelkit import DataFetcher  

# Initialize the DataFetcher with the desired serial port and baud rate  
fetcher = DataFetcher(serial_port="COM3", baud_rate=9600)

2. Fetch Sensor Data

# Fetch data as a Python list  
sensor_data = fetcher.fetch_data(return_as_numpy=False)  
print("Sensor Data:", sensor_data)  

# Fetch data as a NumPy array  
sensor_data_numpy = fetcher.fetch_data(return_as_numpy=True)  
print("Sensor Data (NumPy):", sensor_data_numpy)

3. Log Sensor Data

# Log 10 samples to a CSV file with timestamp and count columns  
fetcher.log_sensor_data(class_label="ClassA", num_samples=10, add_timestamp=True, add_count=True)

CSV Logging Details

The CSV file is generated automatically based on the sensor name (e.g., TemperatureSensor_data_log.csv) and contains the following:

Timestamp: (Optional) Records the time when the data was logged.
Sample Count: (Optional) A sequential counter for each data sample.
Data Columns: Each element in the sensor data array is stored in separate columns (e.g., data_value_1, data_value_2, ...).

The data is saved under a folder named Dataset, with subfolders organized by class_label (if specified).

Real-Time Data Processing Example

from edgemodelkit import DataFetcher  

fetcher = DataFetcher(serial_port="COM3", baud_rate=9600)  

try:  
    while True:  
        # Fetch data as NumPy array  
        sensor_data = fetcher.fetch_data(return_as_numpy=True)  
        print("Received Data:", sensor_data)  

        # Perform custom processing (e.g., feed to a TensorFlow model)  
        # prediction = model.predict(sensor_data)  
        # print("Prediction:", prediction)  
finally:  
    fetcher.close_connection()

Dependencies

EdgeModelKit requires the following Python packages:

numpy
pandas
pyserial
json

Install dependencies with:

pip install numpy pandas pyserial json

Contributing

License

EdgeModelKit is licensed under the MIT License. See the LICENSE file for details.

Support

About EdgeNeuron